Many people have mobile devices, such as mobile phones, tablet devices, and laptop computers that have integrated display touchscreens. Generally, a user of any of these touchscreen-enabled devices can interact with a device by touch input on the touchscreen, such as with finger contact on the touchscreen or with a digital pen, stylus, or other writing-type input device. Most touchscreen devices include a palm rejection solution which is intended to filter out inadvertent contact with the touchscreen, such as when a user rests a part of a hand and/or fingers on the touchscreen while intending a touch input with a digital pen or stylus device. Rather than accepting the inadvertent contact as a touch input from the user intending to initiate some related device action, such as a cursor movement or button selection, the palm rejection solution can reject the hand and/or fingers contact on the touchscreen as inadvertent contact. Otherwise, the inadvertent contact may be processed through to the operating system of the device, and an unwanted device action may then be initiated.
The many different manufacturers of the various types of devices implement palm rejection solutions and features differently. For example, some use host-based processing for software solutions, some use firmware solutions, while others use proprietary solutions. There is no standardized system for competitive analysis of palm rejection performance for these various, different types of current and upcoming devices. Testing the palm rejection solutions and features of devices that have an integrated display touchscreen is an arduous process, and it is not possible to objectively test and measure how effective the palm rejection is for most touch metrics, such as latency, jitter, and finger separation. Current testing techniques simply employ a diverse demographic of users who come together to use a device and comment on how well the device responds to intended contact and inadvertent contact with the touchscreen. This type of testing is not overly accurate, and not scalable or repeatable.